Automated embedding and blending head images

ABSTRACT

An automated process of transferring an object (e.g., the head with hair) from an image into another image, or to a different location in the same image is described. The implementation is a comprehensive and fully automated approach enabling the object&#39;s transfer without intermediate intervention and support from the user. The process automates both steps: the object&#39;s delineation, and its blending into the new background as well.

FIELD OF THE INVENTION

The present invention relates to image processing. More specifically,the present invention relates to automated image processing.

BACKGROUND OF THE INVENTION

In popular photo-editing tools, to move an object (such as a head) fromone place to another, the user conducts tedious manual work ofdelineating the head with all its hair curls. Next, the user performs acomplicated job of blending the extracted object into a new background.These steps are difficult and time-consuming.

In the existing approaches to the head-object delineation from an image(usually a photo), and the subsequent transferring into a differentimage, requires the user intervention in both stages: first, with theguiding for the delineation process, and, secondly, optimizing theembedding of the object into a new background.

SUMMARY OF THE INVENTION

An automated process of transferring an object (e.g., the head withhair) from an image into another image, or to a different location inthe same image is described. The implementation is a comprehensive andfully automated approach enabling the object's transfer withoutintermediate intervention and support from the user. The processautomates both steps: the object's delineation, and its blending intothe new background as well.

In one aspect, a method of automatically transferring an object from afirst location to a second location programmed in a non-transitorymemory of a device comprises automatically delineating the objectincluding recognizing a hairless portion of the object, followed bydelineating a whole hair area; and automatically blending the objectinto a new background. The hairless portion of the object includes aface and a neck area. Recognizing the hairless portion of the objectincludes determining positions of eyes, nose, mouth and chin to separatea face-neck area from a hair area. Delineating the object includesidentifying a hair area by conducting calculations from a face-neck areaoutward. Delineating the object includes repeatedly modifying a hairtemplate to adjust to local hair properties. Delineating the objectincludes continuing hair recognition steps until a whole extent of hair,including hair curls, is found. Blending the object into the newbackground includes utilizing a hair mask. Blending the object into thenew background includes smoothing the hair mask.

In another aspect, a system for automatically transferring an objectfrom a first location to a second location programmed in anon-transitory memory of a device comprises an acquiring deviceconfigured for acquiring an image and a processing device configured forautomatically delineating the object including recognizing a hairlessportion of the object and automatically blending the object into a newbackground to generate a modified image and a display device configuredfor displaying the modified image. The hairless portion of the objectincludes a face and a neck area. Recognizing the hairless portion of theobject includes determining positions of eyes, nose, mouth and chin toseparate a face-neck area from a hair area. Delineating the objectincludes identifying a hair area by conducting calculations from aface-neck area outward. Delineating the object includes repeatedlymodifying a hair template to adjust to local hair properties.Delineating the object includes continuing hair recognition steps untila whole extent of hair, including hair curls, is found. Blending theobject into the new background includes utilizing a hair mask. Blendingthe object into the new background includes smoothing the hair mask.

In another aspect, an apparatus comprises a non-transitory memory forstoring an application, the application for: automatically delineatingan object including recognizing a hairless portion of the object andautomatically blending the object into a new background to generate amodified image, a processing component coupled to the memory, theprocessing component configured for processing the application and adisplay device configured for displaying the modified image. Thehairless portion of the object includes a face and a neck area.Recognizing the hairless portion of the object includes determiningpositions of eyes, nose, mouth and chin to separate a face-neck areafrom a hair area. Delineating the object includes identifying a hairarea by conducting calculations from a face-neck area outward.Delineating the object includes repeatedly modifying a hair template toadjust to local hair properties. Delineating the object includescontinuing hair recognition steps until a whole extent of hair,including hair curls, is found. Blending the object into the newbackground includes utilizing a hair mask. Blending the object into thenew background includes smoothing the hair mask.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1-9 illustrate a sequence of shapes to be analyzed for subobjectlabeling according to some embodiments.

FIG. 10 illustrates a diagram of the method of defining superpixels inthe face area according to some embodiments.

FIG. 11 illustrates three masks used for extended hair embeddingaccording to some embodiments.

FIG. 12 illustrates images related to generating a mask for thin hairareas according to some embodiments.

FIG. 13 illustrates images related to thin lines filter-based embeddingaccording to some embodiments.

FIG. 14 illustrates a data flow diagram of filters to find thin curlsaccording to some embodiments.

FIG. 15 illustrates a flow diagram of embedding an object into a newbackground.

FIG. 16 illustrates artifacts of an old background in a non-adjustedembedding according to some embodiment.

FIG. 17 illustrates images related to eliminating traces of color froman old background according to some embodiments.

FIG. 18 illustrates images related to curl embedding with backgroundstaken into account according to some embodiments.

FIG. 19 illustrates images of reducing artifacts of deep blending bysmoothing the mask's boundary according to some embodiments.

FIG. 20 illustrates a diagram of smoothing an object's mask according tosome embodiments.

FIG. 21 illustrates a flow diagram of two-step smoothing of the object'smask according to some embodiments.

FIG. 22 illustrates a block diagram of an exemplary computing deviceconfigured to implement the embedding and blending method according tosome embodiments.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

An automated process of transferring an object (e.g., the head withhair) from an image into another image, or to a different location inthe same image is described. In some embodiments, the new location forthe object is manually selected by a user (e.g., by clicking with amouse or pointing with a touchscreen), or the new location is determinedautomatically. The implementation is a comprehensive and fully automatedapproach enabling the object's transfer without intermediateintervention and support from the user. The process automates bothsteps: the object's delineation, and its blending into the newbackground as well.

The head-hair delineation process starts with recognition of thehairless portion of the head: face and neck, sometimes the shoulders aswell. The face-neck area are thoroughly delineated. The positions ofeyes, nose, mouth, chin are found in the process and used to separatethe open face-neck areas from the hair area. The hair is a highlyarticulated object that might have a very different shape, color andbrightness between individuals and often varies across the hair area ofeach individual as well. So, to start identifying the hair area, thecalculations are conducted from the face-neck area outward. The hairportion in the immediate vicinity of the open face-neck area is lessarticulated and is used as a first-approach hair template describing thehair properties such as color, brightness, texture. Based on theestimated hair properties, the next imaging methods analyze the haircontinuity by moving outward from the face area. In the progress, thehair template is constantly modified to adjust to the local hairproperties. The hair recognition steps continue until the whole extentof the hair, including hair curls (if they exist) of different size andthickness, is found. Starting the hair recognition from the face andmoving outward helps to orderly handle the local geometrical andcolor-brightness variations.

The hair finding process stops when one or more criteria for a pixel asbelonging to the hair area is not-fulfilled. The criteria include:

Above a threshold change from the allowed hair color-brightness on aboundary with the background objects;

Above a threshold change in the allowed hair texture;

Above a threshold change in the allowed hair geometry (for example, ahair curl is too straight or too long).

The final step of head-hair delineation takes care of thesemitransparent areas of the hair. Normally they are located at theperiphery where the hair density diminishes and the color of backgroundobjects blends with hair color. In these areas, the algorithm collectscolor and brightness of the background and uses this knowledge when theextracted head with its hair have to be embedded into a new background.A special calculation is conducted eliminating the color mixture of theold background and recalculating to the new background to achieve thebest blending result. The blending procedure also includes a spatialsmoothing on the boundary between hair and new background.

Hair Blending

When hair curls vividly stick out, a two-step process is utilized:

1. Generate a mask for the extended hair regions

2. Blend the hair under the mask into the new background

Head and Hair Finding Segmentation

Several image segmentation methods are used to find the head-hair area.The found super-pixels of each segmentation methods are used by adecision-making procedure to find the exact position of the hair area.First, the decision-making process sorts the super pixels by their size.Then, a set of largest super pixels (˜100) is used for the next steps.Second, the largest super-pixels are sorted by their distance from thehead center. Third, the super-pixels are classified by their color,brightness, smoothness in three classes: i) face-neck area, 2) hairarea, 3) background. Finally, the remaining (small) super-pixels areclassified by their belonging to the classes. The exact hair area iscalculated.

Features useful for the exact finding of the hair area:

1) eyes and nose position to understand the head orientation and therespected hair allocation;

2) distribution of shadows on the face to identify the light locationand the respected brightness variety of the hair area;

3) components of the hair areas as they come from different segmentationmethods to identify the color-brightness variety in the hair area.

Explanation to Segmentation Example

Four segmentation methods are used in the examples that follow. Shown isthe sequence of 10 largest super-pixels for each segmentation method,sorted by the distance from the center of the face.

Max(RGB)-based method classifies pixels by their belonging to reddish,greenish, bluish hue;

Color triangle-based method classifies pixels by their color only(brightness is not counted);

Brightness-based classifies pixels into groups based on their absolutebrightness;

Smoothness-based method classifies pixel by the degree of smoothness oftheir surrounding area.

Major Objects Helping in Hair-Segmentation

Eyes and nose position to understand the head orientation and therespected hair allocation;

Distribution of shadows on the face to identify the light location andthe respected brightness variety of the hair area;

Components of the hair areas as they come from different segmentationmethods to identify the color-brightness variety in the hair area.

FIGS. 1-9 illustrate a sequence of shapes to be analyzed for subobjectlabeling according to some embodiments. In FIG. 1, a candidate for hair,eye sockets and eyes, and a candidate for under hair shadow at theforehead are analyzed. In FIG. 2, a new candidate for hair, haircolor-brightness variety and dark hair candidates based on thesmoothness are analyzed. In FIG. 3, hair color-brightness variety, darkand light shadows and shaded area on the face boundary are analyzed. InFIG. 4, hair color-brightness variety, hair candidates from brightnesssegmentation and neck area are analyzed. In FIG. 5, hair color varietyis analyzed. In FIG. 6, hair color variety is analyzed. In FIG. 7, haircolor variety is analyzed. In FIG. 8, eye and eyebrow candidates areanalyzed. In FIG. 9, background candidates are analyzed.

FIG. 10 illustrates a diagram of the method of defining superpixels inthe face area according to some embodiments. A source image and a faceoval mask are input. Max RGB segmentation is implemented. Overlappingsuperpixels are determined. Non-face superpixels are filtered out.Superpixels close or inside the face oval are determined.

When moving a head image into a different background, finding andblending of thin, often semitransparent hair curls is difficult. Tosolve the issue, the exact location of outer hair curls is found, thecolor signature of the background in the vicinity of the curls isdetermined, the color of the semi-transparent curls from the brightnessand the hue of the old background into the brightness and hue of the newbackground are transformed. Blending is smoothed.

FIG. 11 illustrates three masks used for extended hair embeddingaccording to some embodiments. The first mask 1100 crops the objectswith cut-off curls. The second mask 1102 is for the expected curl area.The third mask 1104 is for the hair color.

FIG. 12 illustrates images related to generating a mask for thin hairareas according to some embodiments. The mask is generated in two steps:a segmentation with “haircut” is conducted to specify the basic hairregion, and color and brightness of the haircut are calculated, and thenthe mask for the curls is calculated. Image 1200 is the original image.Image 1202 is a cropped (haircut) object. Image 1204 includes the hairarea to specify the hair color. Image 1206 shows curl mask candidatesbefore removing wrong edges and matching for the hair color.

FIG. 13 illustrates images related to thin lines filter-based embeddingaccording to some embodiments. Image 1300 is the original image. Image1302 is the thin-line filtered image. Image 1304 is the thin-linefiltered image thresholded and added to the object mask. Image 1306 isthe object embedded into a different background.

FIG. 14 illustrates a data flow diagram of filters to find thin curlsaccording to some embodiments. A 3×3 average filter is applied to anoriginal image. The difference between the original image and the 3×3average is determined. A boost is applied to the result which is summedwith the original image. The summed image is sent to a set ofdirectional fine-line filters and a max or min is applied to generate ahair candidate mask.

FIG. 15 illustrates a flow diagram of embedding an object into a newbackground. The hair candidate mask is compared with a threshold and athin-line threshold image is determined. An average hair color (RGB) inthe hair mask is determined. Brightness is determined from a sourceimage. The color image is converted to a gray-level image. Color istaken from the average hair color (RGB), brightness is used from thegray-level image, the mask from the thin-line image, located in the curlarea not belonging to the object mask area, and the brightness of thehair is modified according to the new background. The result is blendedinto the new background.

FIG. 16 illustrates artifacts of an old background in a non-adjustedembedding according to some embodiment.

FIG. 17 illustrates images related to eliminating traces of color froman old background according to some embodiments. Image 1700 is anoriginal image. Image 1702 shows no hair color filtering. Image 1704includes hair color filtering where traces from the old background areeliminated.

For a blending area of the image, it is assumed that the object'stransparency, “a” (alpha-parameter) remains constant in differentbackgrounds. For object intensity, F1, observed in background B1:F1=(1−a)*h+a*B1. For object intensity, F2, observed in background B2:F2=(1−a)*h+a*B2. Intensity, F2, in the new background is equal to:F2=F1+a*(B2−B1). “h” is object intensity before blending with thebackground, and “a” is object transparency.

The described approach uses the alpha-parameter “a” which, asexperiments show, is able to be safely taken equal to 0.2 for any kindof background. The follow-up 1-ring blending with new backgroundimproves the perceptual quality of embedding. The method allows tomaintain the original brightness of the curls regardless of the newbackground.

FIG. 18 illustrates images related to curl embedding with backgroundstaken into account according to some embodiments.

To expedite the calculation of the object's mask, the original image isdownsized. As a result, the obtained object mask looks “jagged” aftermaking the expansion to return back to the original image size.

Smoothing by blending leaves jagged artifacts. Mask smoothing isimplemented by finding an external contour of the mask, smoothing theexternal contour and refilling the mask. FIG. 19 illustrates images ofreducing artifacts of deep blending by smoothing the mask's boundaryaccording to some embodiments. The external boundary of the mask issmoothed by a moving window of 9-pixels.

FIG. 20 illustrates a diagram of smoothing an object's mask according tosome embodiments. A 3×3 moving window is used. Mask pixels in the 3×3window are shown by the white color. In the example, smoothing includesremoving a pixel from the external corner and adding a pixel to theinternal corner. Each (of two) kernels are repeated with 0, 90, 180, 270degrees of rotation, so there are 8 kernels.

FIG. 21 illustrates a flow diagram of two-step smoothing of the object'smask according to some embodiments. The original mask is downsized. Thefilter is applied, which smoothes the image. The mask is expanded. Thefilter is applied again which produces a final smoothed mask.

FIG. 22 illustrates a block diagram of an exemplary computing deviceconfigured to implement the embedding and blending method according tosome embodiments. The computing device 2200 is able to be used toacquire, store, compute, process, communicate and/or display informationsuch as images and videos. In general, a hardware structure suitable forimplementing the computing device 2200 includes a network interface2202, a memory 2204, a processor 2206, I/O device(s) 2208, a bus 2210and a storage device 2212. The choice of processor is not critical aslong as a suitable processor with sufficient speed is chosen. The memory2204 is able to be any conventional computer memory known in the art.The storage device 2212 is able to include a hard drive, CDROM, CDRW,DVD, DVDRW, High Definition disc/drive, ultra-HD drive, flash memorycard or any other storage device. The computing device 2200 is able toinclude one or more network interfaces 2202. An example of a networkinterface includes a network card connected to an Ethernet or other typeof LAN. The I/O device(s) 2208 are able to include one or more of thefollowing: keyboard, mouse, monitor, screen, printer, modem,touchscreen, button interface and other devices. Embedding and blendingmethod application(s) 2230 used to implement the embedding and blendingmethod are likely to be stored in the storage device 2212 and memory2204 and processed as applications are typically processed. More orfewer components shown in FIG. 22 are able to be included in thecomputing device 2200. In some embodiments, embedding and blendingmethod hardware 2220 is included. Although the computing device 2200 inFIG. 22 includes applications 2230 and hardware 2220 for the embeddingand blending method, the embedding and blending method is able to beimplemented on a computing device in hardware, firmware, software or anycombination thereof. For example, in some embodiments, the embedding andblending method applications 2230 are programmed in a memory andexecuted using a processor. In another example, in some embodiments, theembedding and blending method hardware 2220 is programmed hardware logicincluding gates specifically designed to implement the embedding andblending method.

In some embodiments, the embedding and blending method application(s)2230 include several applications and/or modules. In some embodiments,modules include one or more sub-modules as well. In some embodiments,fewer or additional modules are able to be included.

Examples of suitable computing devices include a personal computer, alaptop computer, a computer workstation, a server, a mainframe computer,a handheld computer, a personal digital assistant, a cellular/mobiletelephone, a smart appliance, a gaming console, a digital camera, adigital camcorder, a camera phone, a smart phone, a portable musicplayer, a tablet computer, a mobile device, a video player, a video discwriter/player (e.g., DVD writer/player, high definition discwriter/player, ultra high definition disc writer/player), a television,a home entertainment system, smart jewelry (e.g., smart watch) or anyother suitable computing device.

To utilize the embedding and blending method described herein, a devicesuch as a digital camera/camcorder is used to acquire image/videocontent. The embedding and blending method is automatically used to movean object in the image or video. The embedding and blending method isable to be implemented automatically without user involvement.

In operation, the embedding and blending method is able to be used as animage processing software application, suitable for the use in smartphones, notebooks, computers, game consoles and a portion of the imageprocessing packet, for example. In some embodiments, the embedding andblending method is combined with scene analysis, automatedobject-of-interest finding and inpainting methods. The combined methodis able to be used as a stand-alone application for the automated objectdelineation and its subsequent transfer and embedding into a differentphoto.

Some Embodiments of Automated Embedding and Blending Head Images

-   1. A method of automatically transferring an object from a first    location to a second location programmed in a non-transitory memory    of a device comprising:    -   a. automatically delineating the object including recognizing a        hairless portion of the object, followed by delineating a whole        hair area; and    -   b. automatically blending the object into a new background.-   2. The method of clause 1 wherein the hairless portion of the object    includes a face and a neck area.-   3. The method of clause 1 wherein recognizing the hairless portion    of the object includes determining positions of eyes, nose, mouth    and chin to separate a face-neck area from a hair area.-   4. The method of clause 1 wherein delineating the object includes    identifying a hair area by conducting calculations from a face-neck    area outward.-   5. The method of clause 1 wherein delineating the object includes    repeatedly modifying a hair template to adjust to local hair    properties.-   6. The method of clause 1 wherein delineating the object includes    continuing hair recognition steps until a whole extent of hair,    including hair curls, is found.-   7. The method of clause 1 wherein blending the object into the new    background includes utilizing a hair mask.-   8. The method of clause 7 wherein blending the object into the new    background includes smoothing the hair mask.-   9. A system for automatically transferring an object from a first    location to a second location programmed in a non-transitory memory    of a device comprising:    -   a. an acquiring device configured for acquiring an image; and    -   b. a processing device configured for:        -   i. automatically delineating the object including            recognizing a hairless portion of the object; and        -   ii. automatically blending the object into a new background            to generate a modified image; and    -   c. a display device configured for displaying the modified        image.-   10. The system of clause 9 wherein the hairless portion of the    object includes a face and a neck area.-   11. The system of clause 9 wherein recognizing the hairless portion    of the object includes determining positions of eyes, nose, mouth    and chin to separate a face-neck area from a hair area.-   12. The system of clause 9 wherein delineating the object includes    identifying a hair area by conducting calculations from a face-neck    area outward.-   13. The system of clause 9 wherein delineating the object includes    repeatedly modifying a hair template to adjust to local hair    properties.-   14. The system of clause 9 wherein delineating the object includes    continuing hair recognition steps until a whole extent of hair,    including hair curls, is found.-   15. The system of clause 9 wherein blending the object into the new    background includes utilizing a hair mask.-   16. The system of clause 15 wherein blending the object into the new    background includes smoothing the hair mask.-   17. An apparatus comprising:    -   a. a non-transitory memory for storing an application, the        application for:        -   i. automatically delineating an object including recognizing            a hairless portion of the object; and        -   ii. automatically blending the object into a new background            to generate a modified image;    -   b. a processing component coupled to the memory, the processing        component configured for processing the application; and    -   c. a display device configured for displaying the modified        image.-   18. The apparatus of clause 17 wherein the hairless portion of the    object includes a face and a neck area.-   19. The apparatus of clause 17 wherein recognizing the hairless    portion of the object includes determining positions of eyes, nose,    mouth and chin to separate a face-neck area from a hair area.-   20. The apparatus of clause 17 wherein delineating the object    includes identifying a hair area by conducting calculations from a    face-neck area outward.-   21. The apparatus of clause 17 wherein delineating the object    includes repeatedly modifying a hair template to adjust to local    hair properties.-   22. The apparatus of clause 17 wherein delineating the object    includes continuing hair recognition steps until a whole extent of    hair, including hair curls, is found.-   23. The apparatus of clause 17 wherein blending the object into the    new background includes utilizing a hair mask.-   24. The apparatus of clause 23 wherein blending the object into the    new background includes smoothing the hair mask.

The present invention has been described in terms of specificembodiments incorporating details to facilitate the understanding ofprinciples of construction and operation of the invention. Suchreference herein to specific embodiments and details thereof is notintended to limit the scope of the claims appended hereto. It will bereadily apparent to one skilled in the art that other variousmodifications may be made in the embodiment chosen for illustrationwithout departing from the spirit and scope of the invention as definedby the claims.

What is claimed is:
 1. A method of automatically transferring an objectfrom a first location to a second location programmed in anon-transitory memory of a device comprising: a. automaticallydelineating the object including recognizing a hairless portion of theobject, followed by delineating a whole hair area, wherein delineatingthe object includes identifying a hair area by finding super-pixels in ahead-hair area, sorting the super-pixels by size, sorting a set oflargest super-pixels by distance from a head center and classifying thesuper-pixels; and b. automatically blending the object into a newbackground, wherein delineating the object includes repeatedly modifyinga hair template to adjust to local hair properties.
 2. The method ofclaim 1 wherein the hairless portion of the object includes a face and aneck area.
 3. The method of claim 1 wherein recognizing the hairlessportion of the object includes determining positions of eyes, nose,mouth and chin to separate a face-neck area from the hair area.
 4. Themethod of claim 1 wherein delineating the object includes identifyingthe hair area by conducting calculations from a face-neck area outward.5. The method of claim 1 wherein delineating the object includescontinuing hair recognition steps until a whole extent of hair,including hair curls, is found.
 6. The method of claim 1 whereinblending the object into the new background includes utilizing a hairmask.
 7. The method of claim 6 wherein blending the object into the newbackground includes smoothing the hair mask.
 8. A system forautomatically transferring an object from a first location to a secondlocation programmed in a non-transitory memory of a device comprising:a. an acquiring device configured for acquiring an image; and b. aprocessing device configured for: i. automatically delineating theobject including recognizing a hairless portion of the object, whereindelineating the object includes identifying a hair area by findingsuper-pixels in a head-hair area, sorting the super-pixels by size,sorting a set of largest super-pixels by distance from a head center andclassifying the super-pixels; and ii. automatically blending the objectinto a new background to generate a modified image, wherein delineatingthe object includes repeatedly modifying a hair template to adjust tolocal hair properties; and c. a display device configured for displayingthe modified image.
 9. The system of claim 8 wherein the hairlessportion of the object includes a face and a neck area.
 10. The system ofclaim 8 wherein recognizing the hairless portion of the object includesdetermining positions of eyes, nose, mouth and chin to separate aface-neck area from the hair area.
 11. The system of claim 8 whereindelineating the object includes identifying the hair area by conductingcalculations from a face-neck area outward.
 12. The system of claim 8wherein delineating the object includes continuing hair recognitionsteps until a whole extent of hair, including hair curls, is found. 13.The system of claim 8 wherein blending the object into the newbackground includes utilizing a hair mask.
 14. The system of claim 13wherein blending the object into the new background includes smoothingthe hair mask.
 15. An apparatus comprising: a. a non-transitory memoryfor storing an application, the application for: i. automaticallydelineating an object including recognizing a hairless portion of theobject, wherein delineating the object includes identifying a hair areaby finding super-pixels in a head-hair area, sorting the super-pixels bysize, sorting a set of largest super-pixels by distance from a headcenter and classifying the super-pixels; and ii. automatically blendingthe object into a new background to generate a modified image, whereindelineating the object includes repeatedly modifying a hair template toadjust to local hair properties; b. a processing component coupled tothe memory, the processing component configured for processing theapplication; and c. a display device configured for displaying themodified image.
 16. The apparatus of claim 15 wherein the hairlessportion of the object includes a face and a neck area.
 17. The apparatusof claim 15 wherein recognizing the hairless portion of the objectincludes determining positions of eyes, nose, mouth and chin to separatea face-neck area from the hair area.
 18. The apparatus of claim 15wherein delineating the object includes identifying the hair area byconducting calculations from a face-neck area outward.
 19. The apparatusof claim 15 wherein delineating the object includes continuing hairrecognition steps until a whole extent of hair, including hair curls, isfound.
 20. The apparatus of claim 15 wherein blending the object intothe new background includes utilizing a hair mask.
 21. The apparatus ofclaim 20 wherein blending the object into the new background includessmoothing the hair mask.